On functional central limit theorems of Bayesian nonparametric priors

Luai Al Labadi, Ibrahim Abdelrazeq

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

A general approach to derive the weak convergence, when centered and rescaled, of certain Bayesian nonparametric priors is proposed. This method may be applied to a wide range of processes including, for instance, nondecreasing nonnegative pure jump Lévy processes and normalized nondecreasing nonnegative pure jump Lévy processes with known finite dimensional distributions. Examples clarifying this approach involve the beta process in latent feature models and the Dirichlet process.

Original languageEnglish
Pages (from-to)215-229
Number of pages15
JournalStatistical Methods and Applications
Volume26
Issue number2
DOIs
Publication statusPublished - Jun 1 2017
Externally publishedYes

Keywords

  • Beta process
  • Dirichlet process
  • Lévy processes
  • Nonparametric Bayesian inference
  • Processes with independent increments
  • Quantile process
  • Weak convergence

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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